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http://sci-hub.tw/http://dx.doi.org/10.1016/j.cell.2015.11.001
Personalized Nutrition by Prediction of Glycemic Responses.
Zeevi D1, Korem T1, Zmora N2, Israeli D3, Rothschild D1, Weinberger A1, Ben-Yacov O1, Lador D1, Avnit-Sagi T1, Lotan-Pompan M1, Suez J4, Mahdi JA4, Matot E1, Malka G1, Kosower N1, Rein M1, Zilberman-Schapira G4, Dohnalová L4, Pevsner-Fischer M4, Bikovsky R1, Halpern Z5, Elinav E6, Segal E7.
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Abstract
Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.
TRIAL REGISTRATION:
ClinicalTrials.gov NCT01892956.